Overview

Dataset statistics

Number of variables18
Number of observations26458
Missing cells192211
Missing cells (%)40.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory144.0 B

Variable types

Numeric10
Categorical8

Alerts

2013 is highly correlated with 2014 and 14 other fieldsHigh correlation
2014 is highly correlated with 2013 and 14 other fieldsHigh correlation
2015 is highly correlated with LAT and 12 other fieldsHigh correlation
2016 is highly correlated with 2013 and 11 other fieldsHigh correlation
2017 is highly correlated with LAT and 15 other fieldsHigh correlation
2018 is highly correlated with 2013 and 14 other fieldsHigh correlation
2019 is highly correlated with 2013 and 14 other fieldsHigh correlation
2020 is highly correlated with 2013 and 7 other fieldsHigh correlation
LABEL2020 is highly correlated with 2013 and 14 other fieldsHigh correlation
LABEL2017 is highly correlated with 2013 and 11 other fieldsHigh correlation
LABEL2016 is highly correlated with 2013 and 13 other fieldsHigh correlation
LABEL2018 is highly correlated with 2013 and 11 other fieldsHigh correlation
LABEL2019 is highly correlated with 2013 and 11 other fieldsHigh correlation
LABEL2014 is highly correlated with 2013 and 13 other fieldsHigh correlation
LABEL2013 is highly correlated with 2013 and 13 other fieldsHigh correlation
LABEL2015 is highly correlated with 2013 and 13 other fieldsHigh correlation
LAT is highly correlated with 2015 and 1 other fieldsHigh correlation
LABEL2013 has 24111 (91.1%) missing values Missing
LABEL2014 has 24081 (91.0%) missing values Missing
LABEL2015 has 24080 (91.0%) missing values Missing
LABEL2016 has 24080 (91.0%) missing values Missing
LABEL2017 has 24051 (90.9%) missing values Missing
LABEL2018 has 23977 (90.6%) missing values Missing
LABEL2019 has 23938 (90.5%) missing values Missing
LABEL2020 has 23893 (90.3%) missing values Missing

Reproduction

Analysis started2022-09-22 15:20:40.142185
Analysis finished2022-09-22 15:21:00.672921
Duration20.53 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

LAT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct192
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.40020996
Minimum16.9375
Maximum17.8925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:00.741529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum16.9375
5-th percentile17.0325
Q117.2175
median17.3975
Q317.5775
95-th percentile17.7825
Maximum17.8925
Range0.955
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.2316771372
Coefficient of variation (CV)0.01331461734
Kurtosis-0.9851021179
Mean17.40020996
Median Absolute Deviation (MAD)0.18
Skewness0.05136507924
Sum460374.755
Variance0.05367429588
MonotonicityNot monotonic
2022-09-22T20:51:00.867360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.4775195
 
0.7%
17.4975195
 
0.7%
17.5075194
 
0.7%
17.4925194
 
0.7%
17.5025194
 
0.7%
17.3125193
 
0.7%
17.4875193
 
0.7%
17.3075193
 
0.7%
17.3175192
 
0.7%
17.4725192
 
0.7%
Other values (182)24523
92.7%
ValueCountFrequency (%)
16.93753
 
< 0.1%
16.94256
 
< 0.1%
16.947513
 
< 0.1%
16.952517
 
0.1%
16.957522
 
0.1%
16.962529
0.1%
16.967541
0.2%
16.972554
0.2%
16.977556
0.2%
16.982567
0.3%
ValueCountFrequency (%)
17.89253
 
< 0.1%
17.88759
 
< 0.1%
17.882510
 
< 0.1%
17.877511
 
< 0.1%
17.872518
0.1%
17.867528
0.1%
17.862531
0.1%
17.857536
0.1%
17.852541
0.2%
17.847542
0.2%

LON
Real number (ℝ≥0)

Distinct208
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.47750121
Minimum78.0075
Maximum79.0425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:00.997838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum78.0075
5-th percentile78.1125
Q178.2775
median78.4775
Q378.6675
95-th percentile78.8775
Maximum79.0425
Range1.035
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation0.2377368942
Coefficient of variation (CV)0.003029363709
Kurtosis-0.9561032173
Mean78.47750121
Median Absolute Deviation (MAD)0.195
Skewness0.1112356494
Sum2076357.727
Variance0.05651883084
MonotonicityIncreasing
2022-09-22T20:51:01.120906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.5225180
 
0.7%
78.5475179
 
0.7%
78.5175179
 
0.7%
78.5125179
 
0.7%
78.4775179
 
0.7%
78.48251178
 
0.7%
78.5425178
 
0.7%
78.4625178
 
0.7%
78.4675177
 
0.7%
78.4725177
 
0.7%
Other values (198)24674
93.3%
ValueCountFrequency (%)
78.00753
 
< 0.1%
78.01254
 
< 0.1%
78.01756
 
< 0.1%
78.02259
 
< 0.1%
78.027512
 
< 0.1%
78.032515
 
0.1%
78.0375119
0.1%
78.042521
0.1%
78.047527
0.1%
78.0525145
0.2%
ValueCountFrequency (%)
79.04251
 
< 0.1%
79.037513
 
< 0.1%
79.03255
 
< 0.1%
79.02757
< 0.1%
79.022511
< 0.1%
79.017511
< 0.1%
79.012511
< 0.1%
79.007512
< 0.1%
79.002512
< 0.1%
78.9975114
0.1%

2013
Real number (ℝ)

HIGH CORRELATION

Distinct13450
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4207330437
Minimum-0.03517
Maximum0.67606
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size206.8 KiB
2022-09-22T20:51:01.264774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.03517
5-th percentile0.3181195
Q10.39986
median0.42561
Q30.4495
95-th percentile0.49527
Maximum0.67606
Range0.71123
Interquartile range (IQR)0.04964

Descriptive statistics

Standard deviation0.05464817167
Coefficient of variation (CV)0.1298879955
Kurtosis4.478441875
Mean0.4207330437
Median Absolute Deviation (MAD)0.024865
Skewness-1.056008228
Sum11131.75487
Variance0.002986422667
MonotonicityNot monotonic
2022-09-22T20:51:01.422417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4229112
 
< 0.1%
0.4300710
 
< 0.1%
0.42819
 
< 0.1%
0.42519
 
< 0.1%
0.434239
 
< 0.1%
0.426859
 
< 0.1%
0.413639
 
< 0.1%
0.430559
 
< 0.1%
0.43819
 
< 0.1%
0.425779
 
< 0.1%
Other values (13440)26364
99.6%
ValueCountFrequency (%)
-0.035171
< 0.1%
-0.002671
< 0.1%
0.00121
< 0.1%
0.006311
< 0.1%
0.056111
< 0.1%
0.056771
< 0.1%
0.057381
< 0.1%
0.057581
< 0.1%
0.059081
< 0.1%
0.063551
< 0.1%
ValueCountFrequency (%)
0.676061
< 0.1%
0.673751
< 0.1%
0.666381
< 0.1%
0.663171
< 0.1%
0.660251
< 0.1%
0.659811
< 0.1%
0.65671
< 0.1%
0.656651
< 0.1%
0.6561
< 0.1%
0.646811
< 0.1%

2014
Real number (ℝ)

HIGH CORRELATION

Distinct14576
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4159277769
Minimum-0.04602
Maximum0.68919
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)< 0.1%
Memory size206.8 KiB
2022-09-22T20:51:01.560806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.04602
5-th percentile0.3107595
Q10.38753
median0.41967
Q30.44939
95-th percentile0.5026415
Maximum0.68919
Range0.73521
Interquartile range (IQR)0.06186

Descriptive statistics

Standard deviation0.05989031225
Coefficient of variation (CV)0.1439920957
Kurtosis3.176856273
Mean0.4159277769
Median Absolute Deviation (MAD)0.030745
Skewness-0.6312590202
Sum11004.61712
Variance0.003586849502
MonotonicityNot monotonic
2022-09-22T20:51:01.681222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43229
 
< 0.1%
0.416819
 
< 0.1%
0.432499
 
< 0.1%
0.409178
 
< 0.1%
0.411718
 
< 0.1%
0.404348
 
< 0.1%
0.438028
 
< 0.1%
0.417158
 
< 0.1%
0.39778
 
< 0.1%
0.41688
 
< 0.1%
Other values (14566)26375
99.7%
ValueCountFrequency (%)
-0.046021
< 0.1%
-0.024431
< 0.1%
-0.019181
< 0.1%
-0.008571
< 0.1%
0.015341
< 0.1%
0.018691
< 0.1%
0.023251
< 0.1%
0.023851
< 0.1%
0.044971
< 0.1%
0.050251
< 0.1%
ValueCountFrequency (%)
0.689191
< 0.1%
0.687281
< 0.1%
0.685491
< 0.1%
0.678461
< 0.1%
0.674871
< 0.1%
0.669911
< 0.1%
0.664991
< 0.1%
0.656091
< 0.1%
0.654851
< 0.1%
0.652581
< 0.1%

2015
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14213
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3996727224
Minimum0.00144
Maximum0.69087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:01.817122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.00144
5-th percentile0.310058
Q10.3705225
median0.40068
Q30.4306475
95-th percentile0.485193
Maximum0.69087
Range0.68943
Interquartile range (IQR)0.060125

Descriptive statistics

Standard deviation0.05570459149
Coefficient of variation (CV)0.1393755149
Kurtosis2.543939814
Mean0.3996727224
Median Absolute Deviation (MAD)0.03009
Skewness-0.2240419846
Sum10574.54089
Variance0.003103001513
MonotonicityNot monotonic
2022-09-22T20:51:01.936439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.405459
 
< 0.1%
0.406958
 
< 0.1%
0.399858
 
< 0.1%
0.396088
 
< 0.1%
0.410968
 
< 0.1%
0.372168
 
< 0.1%
0.409198
 
< 0.1%
0.405617
 
< 0.1%
0.400317
 
< 0.1%
0.422977
 
< 0.1%
Other values (14203)26380
99.7%
ValueCountFrequency (%)
0.001441
< 0.1%
0.002181
< 0.1%
0.050651
< 0.1%
0.05811
< 0.1%
0.07411
< 0.1%
0.074391
< 0.1%
0.081711
< 0.1%
0.082231
< 0.1%
0.090371
< 0.1%
0.092171
< 0.1%
ValueCountFrequency (%)
0.690871
< 0.1%
0.684711
< 0.1%
0.681821
< 0.1%
0.670461
< 0.1%
0.669311
< 0.1%
0.662471
< 0.1%
0.653821
< 0.1%
0.65311
< 0.1%
0.651431
< 0.1%
0.648331
< 0.1%

2016
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13317
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3787998934
Minimum0.0774
Maximum0.68354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:02.064874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.0774
5-th percentile0.294557
Q10.3545625
median0.379795
Q30.4043675
95-th percentile0.455823
Maximum0.68354
Range0.60614
Interquartile range (IQR)0.049805

Descriptive statistics

Standard deviation0.05016705066
Coefficient of variation (CV)0.132436813
Kurtosis2.827113212
Mean0.3787998934
Median Absolute Deviation (MAD)0.02484
Skewness-0.155404131
Sum10022.28758
Variance0.002516732972
MonotonicityNot monotonic
2022-09-22T20:51:02.184608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3665610
 
< 0.1%
0.3760810
 
< 0.1%
0.4044110
 
< 0.1%
0.3717810
 
< 0.1%
0.390289
 
< 0.1%
0.364749
 
< 0.1%
0.370899
 
< 0.1%
0.389399
 
< 0.1%
0.388289
 
< 0.1%
0.375099
 
< 0.1%
Other values (13307)26364
99.6%
ValueCountFrequency (%)
0.07741
< 0.1%
0.0841
< 0.1%
0.087641
< 0.1%
0.109531
< 0.1%
0.116071
< 0.1%
0.135311
< 0.1%
0.144221
< 0.1%
0.15331
< 0.1%
0.154121
< 0.1%
0.157511
< 0.1%
ValueCountFrequency (%)
0.683541
< 0.1%
0.677681
< 0.1%
0.665761
< 0.1%
0.653071
< 0.1%
0.649251
< 0.1%
0.63891
< 0.1%
0.630491
< 0.1%
0.626031
< 0.1%
0.625861
< 0.1%
0.624441
< 0.1%

2017
Real number (ℝ)

HIGH CORRELATION

Distinct14522
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4157635452
Minimum-0.03609
Maximum0.6929
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)< 0.1%
Memory size206.8 KiB
2022-09-22T20:51:02.338840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.03609
5-th percentile0.308447
Q10.3874425
median0.418925
Q30.45034
95-th percentile0.5029845
Maximum0.6929
Range0.72899
Interquartile range (IQR)0.0628975

Descriptive statistics

Standard deviation0.06016712426
Coefficient of variation (CV)0.1447147662
Kurtosis3.263448593
Mean0.4157635452
Median Absolute Deviation (MAD)0.03144
Skewness-0.7126709086
Sum11000.27188
Variance0.003620082842
MonotonicityNot monotonic
2022-09-22T20:51:02.465421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.437399
 
< 0.1%
0.441569
 
< 0.1%
0.445548
 
< 0.1%
0.415288
 
< 0.1%
0.395278
 
< 0.1%
0.441478
 
< 0.1%
0.411778
 
< 0.1%
0.422818
 
< 0.1%
0.410028
 
< 0.1%
0.428688
 
< 0.1%
Other values (14512)26376
99.7%
ValueCountFrequency (%)
-0.036091
< 0.1%
-0.031171
< 0.1%
-0.007331
< 0.1%
-0.004691
< 0.1%
-0.000881
< 0.1%
0.009471
< 0.1%
0.009811
< 0.1%
0.016321
< 0.1%
0.020911
< 0.1%
0.040061
< 0.1%
ValueCountFrequency (%)
0.69291
< 0.1%
0.679711
< 0.1%
0.67831
< 0.1%
0.672121
< 0.1%
0.669591
< 0.1%
0.66921
< 0.1%
0.667141
< 0.1%
0.664181
< 0.1%
0.662851
< 0.1%
0.661771
< 0.1%

2018
Real number (ℝ)

HIGH CORRELATION

Distinct13790
Distinct (%)52.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3843198458
Minimum-0.05067
Maximum0.69356
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size206.8 KiB
2022-09-22T20:51:02.757858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.05067
5-th percentile0.2908755
Q10.35931
median0.38671
Q30.4124375
95-th percentile0.4638765
Maximum0.69356
Range0.74423
Interquartile range (IQR)0.0531275

Descriptive statistics

Standard deviation0.05400839722
Coefficient of variation (CV)0.140529816
Kurtosis3.939374132
Mean0.3843198458
Median Absolute Deviation (MAD)0.02648
Skewness-0.359633358
Sum10168.33448
Variance0.00291690697
MonotonicityNot monotonic
2022-09-22T20:51:02.879691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3720910
 
< 0.1%
0.3889510
 
< 0.1%
0.3995110
 
< 0.1%
0.3949810
 
< 0.1%
0.393659
 
< 0.1%
0.380929
 
< 0.1%
0.397759
 
< 0.1%
0.408119
 
< 0.1%
0.379879
 
< 0.1%
0.389079
 
< 0.1%
Other values (13780)26364
99.6%
ValueCountFrequency (%)
-0.050671
< 0.1%
-0.032461
< 0.1%
-0.030061
< 0.1%
0.004581
< 0.1%
0.012151
< 0.1%
0.019261
< 0.1%
0.021241
< 0.1%
0.02751
< 0.1%
0.028241
< 0.1%
0.028491
< 0.1%
ValueCountFrequency (%)
0.693561
< 0.1%
0.693381
< 0.1%
0.689861
< 0.1%
0.677551
< 0.1%
0.676331
< 0.1%
0.666991
< 0.1%
0.662711
< 0.1%
0.66081
< 0.1%
0.66061
< 0.1%
0.658191
< 0.1%

2019
Real number (ℝ)

HIGH CORRELATION

Distinct13812
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3808315625
Minimum-0.02742
Maximum0.70108
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size206.8 KiB
2022-09-22T20:51:03.010795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.02742
5-th percentile0.286988
Q10.35572
median0.38241
Q30.40886
95-th percentile0.4620815
Maximum0.70108
Range0.7285
Interquartile range (IQR)0.05314

Descriptive statistics

Standard deviation0.05367306228
Coefficient of variation (CV)0.1409364863
Kurtosis3.115297817
Mean0.3808315625
Median Absolute Deviation (MAD)0.02656
Skewness-0.2016911427
Sum10076.04148
Variance0.002880797614
MonotonicityNot monotonic
2022-09-22T20:51:03.132735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3619510
 
< 0.1%
0.399129
 
< 0.1%
0.361939
 
< 0.1%
0.379739
 
< 0.1%
0.371919
 
< 0.1%
0.382979
 
< 0.1%
0.38299
 
< 0.1%
0.379558
 
< 0.1%
0.386258
 
< 0.1%
0.372688
 
< 0.1%
Other values (13802)26370
99.7%
ValueCountFrequency (%)
-0.027421
< 0.1%
-0.023121
< 0.1%
0.032511
< 0.1%
0.034231
< 0.1%
0.0551
< 0.1%
0.059781
< 0.1%
0.063121
< 0.1%
0.076011
< 0.1%
0.076581
< 0.1%
0.080911
< 0.1%
ValueCountFrequency (%)
0.701081
< 0.1%
0.693581
< 0.1%
0.679771
< 0.1%
0.678531
< 0.1%
0.675471
< 0.1%
0.66941
< 0.1%
0.667051
< 0.1%
0.662361
< 0.1%
0.661741
< 0.1%
0.657971
< 0.1%

2020
Real number (ℝ≥0)

HIGH CORRELATION

Distinct14641
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4336901674
Minimum0.11012
Maximum0.70133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size206.8 KiB
2022-09-22T20:51:03.263694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.11012
5-th percentile0.314587
Q10.40704
median0.438895
Q30.46806
95-th percentile0.5231915
Maximum0.70133
Range0.59121
Interquartile range (IQR)0.06102

Descriptive statistics

Standard deviation0.06068388076
Coefficient of variation (CV)0.1399245021
Kurtosis2.100685553
Mean0.4336901674
Median Absolute Deviation (MAD)0.030335
Skewness-0.6852405518
Sum11474.57445
Variance0.003682533384
MonotonicityNot monotonic
2022-09-22T20:51:03.380785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4550111
 
< 0.1%
0.452229
 
< 0.1%
0.433399
 
< 0.1%
0.447258
 
< 0.1%
0.425178
 
< 0.1%
0.437768
 
< 0.1%
0.47218
 
< 0.1%
0.42798
 
< 0.1%
0.463078
 
< 0.1%
0.445467
 
< 0.1%
Other values (14631)26374
99.7%
ValueCountFrequency (%)
0.110121
< 0.1%
0.110271
< 0.1%
0.124511
< 0.1%
0.125981
< 0.1%
0.134941
< 0.1%
0.13731
< 0.1%
0.138271
< 0.1%
0.140781
< 0.1%
0.141331
< 0.1%
0.142491
< 0.1%
ValueCountFrequency (%)
0.701331
< 0.1%
0.70041
< 0.1%
0.700011
< 0.1%
0.680961
< 0.1%
0.680371
< 0.1%
0.675261
< 0.1%
0.674371
< 0.1%
0.671581
< 0.1%
0.669551
< 0.1%
0.665811
< 0.1%

LABEL2013
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24111
Missing (%)91.1%
Memory size206.8 KiB
Urban
2320 
Water
 
27

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11735
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2320
 
8.8%
Water27
 
0.1%
(Missing)24111
91.1%

Length

2022-09-22T20:51:03.490891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:03.577879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2320
98.8%
water27
 
1.2%

Most occurring characters

ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9388
80.0%
Uppercase Letter2347
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2347
25.0%
a2347
25.0%
b2320
24.7%
n2320
24.7%
t27
 
0.3%
e27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2320
98.8%
W27
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin11735
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11735
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2347
20.0%
a2347
20.0%
U2320
19.8%
b2320
19.8%
n2320
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

LABEL2014
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24081
Missing (%)91.0%
Memory size206.8 KiB
Urban
2350 
Water
 
27

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11885
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2350
 
8.9%
Water27
 
0.1%
(Missing)24081
91.0%

Length

2022-09-22T20:51:03.651699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:03.738071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2350
98.9%
water27
 
1.1%

Most occurring characters

ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9508
80.0%
Uppercase Letter2377
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2377
25.0%
a2377
25.0%
b2350
24.7%
n2350
24.7%
t27
 
0.3%
e27
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2350
98.9%
W27
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Latin11885
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2377
20.0%
a2377
20.0%
U2350
19.8%
b2350
19.8%
n2350
19.8%
W27
 
0.2%
t27
 
0.2%
e27
 
0.2%

LABEL2015
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24080
Missing (%)91.0%
Memory size206.8 KiB
Urban
2356 
Water
 
22

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11890
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2356
 
8.9%
Water22
 
0.1%
(Missing)24080
91.0%

Length

2022-09-22T20:51:03.812628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:03.898223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2356
99.1%
water22
 
0.9%

Most occurring characters

ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9512
80.0%
Uppercase Letter2378
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2378
25.0%
a2378
25.0%
b2356
24.8%
n2356
24.8%
t22
 
0.2%
e22
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
U2356
99.1%
W22
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin11890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2356
19.8%
b2356
19.8%
n2356
19.8%
W22
 
0.2%
t22
 
0.2%
e22
 
0.2%

LABEL2016
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24080
Missing (%)91.0%
Memory size206.8 KiB
Urban
2360 
Water
 
18

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11890
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2360
 
8.9%
Water18
 
0.1%
(Missing)24080
91.0%

Length

2022-09-22T20:51:03.972159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:04.059590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2360
99.2%
water18
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9512
80.0%
Uppercase Letter2378
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2378
25.0%
a2378
25.0%
b2360
24.8%
n2360
24.8%
t18
 
0.2%
e18
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
U2360
99.2%
W18
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin11890
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2378
20.0%
a2378
20.0%
U2360
19.8%
b2360
19.8%
n2360
19.8%
W18
 
0.2%
t18
 
0.2%
e18
 
0.2%

LABEL2017
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing24051
Missing (%)90.9%
Memory size206.8 KiB
Urban
2371 
Water
 
36

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12035
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2371
 
9.0%
Water36
 
0.1%
(Missing)24051
90.9%

Length

2022-09-22T20:51:04.133588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:04.221230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2371
98.5%
water36
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9628
80.0%
Uppercase Letter2407
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2407
25.0%
a2407
25.0%
b2371
24.6%
n2371
24.6%
t36
 
0.4%
e36
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
U2371
98.5%
W36
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Latin12035
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12035
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2407
20.0%
a2407
20.0%
U2371
19.7%
b2371
19.7%
n2371
19.7%
W36
 
0.3%
t36
 
0.3%
e36
 
0.3%

LABEL2018
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23977
Missing (%)90.6%
Memory size206.8 KiB
Urban
2432 
Water
 
49

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12405
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2432
 
9.2%
Water49
 
0.2%
(Missing)23977
90.6%

Length

2022-09-22T20:51:04.298142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:04.384414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2432
98.0%
water49
 
2.0%

Most occurring characters

ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9924
80.0%
Uppercase Letter2481
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2481
25.0%
a2481
25.0%
b2432
24.5%
n2432
24.5%
t49
 
0.5%
e49
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
U2432
98.0%
W49
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12405
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII12405
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2481
20.0%
a2481
20.0%
U2432
19.6%
b2432
19.6%
n2432
19.6%
W49
 
0.4%
t49
 
0.4%
e49
 
0.4%

LABEL2019
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23938
Missing (%)90.5%
Memory size206.8 KiB
Urban
2485 
Water
 
35

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12600
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2485
 
9.4%
Water35
 
0.1%
(Missing)23938
90.5%

Length

2022-09-22T20:51:04.460464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:04.550389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2485
98.6%
water35
 
1.4%

Most occurring characters

ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10080
80.0%
Uppercase Letter2520
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2520
25.0%
a2520
25.0%
b2485
24.7%
n2485
24.7%
t35
 
0.3%
e35
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
U2485
98.6%
W35
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin12600
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII12600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2520
20.0%
a2520
20.0%
U2485
19.7%
b2485
19.7%
n2485
19.7%
W35
 
0.3%
t35
 
0.3%
e35
 
0.3%

LABEL2020
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing23893
Missing (%)90.3%
Memory size206.8 KiB
Urban
2550 
Water
 
15

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters12825
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowUrban
4th rowUrban
5th rowUrban

Common Values

ValueCountFrequency (%)
Urban2550
 
9.6%
Water15
 
0.1%
(Missing)23893
90.3%

Length

2022-09-22T20:51:04.628117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-22T20:51:04.717286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
urban2550
99.4%
water15
 
0.6%

Most occurring characters

ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10260
80.0%
Uppercase Letter2565
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r2565
25.0%
a2565
25.0%
b2550
24.9%
n2550
24.9%
t15
 
0.1%
e15
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
U2550
99.4%
W15
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin12825
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r2565
20.0%
a2565
20.0%
U2550
19.9%
b2550
19.9%
n2550
19.9%
W15
 
0.1%
t15
 
0.1%
e15
 
0.1%

Interactions

2022-09-22T20:50:58.534868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:47.298269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:48.647387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:49.887702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.165892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:52.518419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.693839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.851241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:56.188485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.367818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.641459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:47.411996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:48.755984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.000393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.280442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:52.629602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.804615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.964730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:56.298545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.482301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.752294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:47.530692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:48.865912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.136762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.400391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:52.742113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.919153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.078935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:56.414682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.596017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.868079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:47.645098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:48.983609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.269220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.519694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:52.862164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.036540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.199726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:56.533379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.713797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.983298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:47.763450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:49.098174image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.399679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.639567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:52.982950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.153168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.319717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:56.651915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.831783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:59.098025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:47.879874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:49.214967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.525432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.759201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.102671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.270010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.437687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:56.775702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.950717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:59.211649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:47.991816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:49.357795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.652265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.876124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.218930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.387371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.553331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:56.894056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.065337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:59.326851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:48.109333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:49.499401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.772737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.996357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.338595image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.505088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.671744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.013664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.183125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:59.441507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:48.227883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:49.636409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:50.906961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:52.116566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.458574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.621558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.791371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.132572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.299780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:59.555948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:48.343688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:49.758782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:51.041783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:52.235868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:53.577656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:54.737125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:55.912332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:57.251052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-09-22T20:50:58.419915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-09-22T20:51:04.794636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-22T20:51:04.933969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-22T20:51:05.071811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-22T20:51:05.208018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-22T20:51:05.518691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-22T20:50:59.905114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-22T20:51:00.195531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-22T20:51:00.410480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-22T20:51:00.559306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

LATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
017.322578.00750.357290.371160.361330.356940.372480.342030.319610.37996NoneNoneNoneNoneNoneNoneNoneNone
117.327578.00750.380090.385300.383880.380770.395320.351600.325870.39876NoneNoneNoneNoneNoneNoneNoneNone
217.332578.00750.401480.416390.407400.394640.415690.364430.334750.42572NoneNoneNoneNoneNoneNoneNoneNone
317.322578.01250.359810.375910.357740.348190.374570.325860.310500.38240NoneNoneNoneNoneNoneNoneNoneNone
417.327578.01250.379710.390270.387100.383170.399490.337890.315400.38711NoneNoneNoneNoneNoneNoneNoneNone
517.332578.01250.390880.412300.403040.397970.420670.345340.320560.41159NoneNoneNoneNoneNoneNoneNoneNone
617.337578.01250.402260.416910.416880.402040.441450.362540.334840.44026NoneNoneNoneNoneNoneNoneNoneNone
717.312578.01750.358550.391180.358600.346540.405620.341580.320990.39045NoneNoneNoneNoneNoneNoneNoneNone
817.317578.01750.352390.384640.348550.336440.371750.322150.300930.38475NoneNoneNoneNoneNoneNoneNoneNone
917.322578.01750.370790.384110.353260.347260.378900.321500.308830.38772NoneNoneNoneNoneNoneNoneNoneNone

Last rows

LATLON20132014201520162017201820192020LABEL2013LABEL2014LABEL2015LABEL2016LABEL2017LABEL2018LABEL2019LABEL2020
2644817.517579.027500.445620.367910.358290.364540.429400.360700.395070.44479NoneNoneNoneNoneNoneNoneNoneNone
2644917.492579.032500.407070.356400.345200.326410.378290.354320.372460.42446NoneNoneNoneNoneNoneNoneNoneNone
2645017.497579.032500.419410.365490.359740.340700.399640.371680.384380.43044NoneNoneNoneNoneNoneNoneNoneNone
2645117.502579.032500.431730.384520.381830.362240.415920.389460.398390.44594NoneNoneNoneNoneNoneNoneNoneNone
2645217.507579.032500.440640.403380.391870.374330.424190.402390.406590.44797NoneNoneNoneNoneNoneNoneNoneNone
2645317.512579.032500.442560.394130.388300.377340.428310.386590.401210.44743NoneNoneNoneNoneNoneNoneNoneNone
2645417.497579.037510.418960.361060.362720.346240.394450.371770.383860.43411NoneNoneNoneNoneNoneNoneNoneNone
2645517.502579.037510.429900.382450.381960.364190.418350.389220.398900.45123NoneNoneNoneNoneNoneNoneNoneNone
2645617.507579.037510.438430.404990.393340.367920.427120.405600.417350.46986NoneNoneNoneNoneNoneNoneNoneNone
2645717.507579.042500.432040.395280.381660.353010.418620.400420.415020.47227NoneNoneNoneNoneNoneNoneNoneNone